MM_est: Estimation of single Michaelis-Menten constant

Description Usage Arguments Details Value Examples

View source: R/single_MM.R

Description

The function estimates MM constant using input data set and enzyme, substrate concentration, and catalytic constant.

Usage

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MM_est(method = T, time, species, enz, subs, MM, catal, tun = 2.4, std,
  nrepeat, jump = 1, burning = 0, MM_m = 1, MM_v = 1e+06)

Arguments

method

method selection: T=TQ model, F=SQ model(default = T)

time

observed time interval

species

observed trajectory of product

enz

enzyme concentration

subs

substrate concentration

MM

initial value of MM constant

catal

true value of catalytic constant

tun

tunning constant of MH algorithm (default=2.4)

std

standard deviation of proposal distribution (if =0, caclulated by Opt. function)

nrepeat

total number of iteration (default=10000)

jump

length of distance (default =1)

burning

lenth of burning period (default =0)

MM_m

prior mean of gamma prior (default =1)

MM_v

prior variance of gamma prior (default =10000)

Details

The function MM_est generates a set of Markov Chain Monte Carlo simulation samples from posterior distribution of MM constant of enzyme kinetics model. Because the function considers MM constant as a parameter to be estimated, the user should input three constants of enzyme concentration, substrate concentration and catalytic constant. prior information for MM constant can be given. The turning constant and standard deviation can be set to controlled proper mixing and acceptance ratio of MM constant from it's posterior distribution. Posterior samples are only stored with fixed interval according to set "jump" to reduce serial correlation The initial iterations are removed for convergence. The burning is set the length of initial iterations.

Value

A vector of posterior samples of Michaelis-Menten constant

Examples

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data("Chymo_low")
time1=Chymo_low[,1]
species1=Chymo_low[,2]
 Chymotrypsin.mm<-MM_est(method=TRUE,time=time1,species=species1,enz=4.4e+7,subs=4.4e+7
 ,MM=10000,catal=0.051,tun=2.4,std=8e+7 ,nrepeat=1000,jump=10
 ,burning=0,MM_m=1,MM_v=1e+10)

EKMCMC documentation built on May 2, 2019, 3:47 p.m.

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